1. Field of the Invention
The present invention relates to an image processing apparatus used for recognizing parts and other targets by using vision sensors and, more particularly, to an image processing apparatus that uses images to recognize the positions and orientations of targets that may be oriented in various directions.
2. Description of the Related Art
To enable a robot or another automatic machine to handle a target object that is not positioned precisely, such as a part, the target is imaged and its position, orientation, and so on are recognized from the resulting image data. This technique is used in many applications, but the wider the range of possible orientations of the target, the more difficult orientation recognition becomes. If the target faces in an arbitrary three-dimensional direction in a pile of parts, for example, its orientation is very difficult to recognize.
To address the above problem, Japanese Patent Application Laid-open No. 2000-288974 proposes a recognition method in which sample images of a target are captured from various directions in advance. The input image is compared with the sample images one by one to recognize the orientation of the target. The sample image providing the best match with the input image is selected and the orientation of the target is determined from the imaging direction of the selected sample image.
In this target orientation recognition method, the critical issue is to maximize the accuracy of the comparison. One of the factors lowering the comparison accuracy is a non-uniform background (non-target area) present in the input image used for recognition. When the sample images are obtained, the target is imaged under ideal conditions with a uniform background having uniform color and uniform brightness. At the site where the target is actually recognized, however, the background of the obtained input image includes unpredictable clutter such as objects other than the target to be recognized. A method of removing this interfering background is proposed by H. Murase and K. Nayar in ‘Detection of 3D objects in cluttered scenes using hierarchical eigenspace’, Pattern Recognition Letters 18, Vol. 14, No. 1, pp. 375-384, 1997. In this method, a window function is created as the product set of the area of a target of all the sample images and only the part of the image included in the window (product set) represented by the window function is used for comparison with the sample images.
As described above, one factor that lowers the sample image comparison accuracy is an irregular background coexisting with the target in the input image. If the comparison is carried out under conditions in which the effect of the background is removed, the recognition accuracy should improve, but the background removal method presented in the above reference is problematic in that the intersection window is often too narrow and the recognition accuracy may actually be worsened.